Multifunctional aggregation network of cell nuclei segmentation aiming histopathological diagnosis assistance: A new MA-Net construction.

ABSTRACT: Automated diagnostic systems can enhance the accuracy and efficiency of pathological diagnoses, nuclear segmentation plays a crucial role in computer-aided diagnosis systems for histopathology. However, achieving accurate nuclear segmentation is challenging due to the complex background tissue structures and significant variations in cell morphology and size in pathological images. In this study, we have proposed a U-Net based deep learning model, called MA-Net(Multifunctional Aggregation Network), to accurately segmenting nuclei from H&E stained images. In contrast to previous studies that focused on improving a single module of the network, we applied feature fusion modules, attention gate units, and atrous spatial pyramid pooling to the encoder and decoder, skip connections, and bottlen

SUBMITTER: Pu Q 

PROVIDER: S-EPMC11379384 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature